TY - GEN
T1 - Data mining algorithms as a service in the cloud exploiting relational database systems
AU - Ordonez, Carlos
AU - García-García, Javier
AU - Garcia-Alvarado, Carlos
AU - Cabrera, Wellington
AU - Baladandayuthapani, Veerabhadran
AU - Quraishi, Mohammed S.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - We present a novel cloud system based on DBMS technology, where data mining algorithms are offered as a service. A local DBMS connects to the cloud and the cloud system returns computed data mining models as small relational tables that are archived and which can be easily transferred, queried and integrated with the client database. Unlike other analytic systems, our solution is not based on MapRe-duce. Our system avoids exporting large tables outside the local DBMS and thus it avoids transmitting large volumes of data to the cloud. The system offers three processing modes: local, cloud and hybrid, where a linear cost model is used to choose processing mode. In hybrid mode processing is split between the local DBMS and the cloud DBMS. Our system has a job scheduler with FIFO, SJF and RR policies to enhance response time and get partial results early. The cloud DBMS performs dynamic job scheduling, model computation and model archive management. Our system incorporates several optimizations: local data set summarization with sufficient statistics, sampling, caching matrices in RAM and selectively transmitting small matrices, back and forth. We show that in general the most efficient computing mechanism is hybrid processing: summarizing or sampling the data set in the local DBMS, transferring small matrices back and forth, leaving mathematically complex methods as a task for the cloud DBMS.
AB - We present a novel cloud system based on DBMS technology, where data mining algorithms are offered as a service. A local DBMS connects to the cloud and the cloud system returns computed data mining models as small relational tables that are archived and which can be easily transferred, queried and integrated with the client database. Unlike other analytic systems, our solution is not based on MapRe-duce. Our system avoids exporting large tables outside the local DBMS and thus it avoids transmitting large volumes of data to the cloud. The system offers three processing modes: local, cloud and hybrid, where a linear cost model is used to choose processing mode. In hybrid mode processing is split between the local DBMS and the cloud DBMS. Our system has a job scheduler with FIFO, SJF and RR policies to enhance response time and get partial results early. The cloud DBMS performs dynamic job scheduling, model computation and model archive management. Our system incorporates several optimizations: local data set summarization with sufficient statistics, sampling, caching matrices in RAM and selectively transmitting small matrices, back and forth. We show that in general the most efficient computing mechanism is hybrid processing: summarizing or sampling the data set in the local DBMS, transferring small matrices back and forth, leaving mathematically complex methods as a task for the cloud DBMS.
KW - Algorithms
KW - Languages
KW - Performance
KW - Theory
UR - http://www.scopus.com/inward/record.url?scp=84880559186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880559186&partnerID=8YFLogxK
U2 - 10.1145/2463676.2465240
DO - 10.1145/2463676.2465240
M3 - Conference contribution
AN - SCOPUS:84880559186
SN - 9781450320375
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1001
EP - 1004
BT - SIGMOD 2013 - International Conference on Management of Data
T2 - 2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013
Y2 - 22 June 2013 through 27 June 2013
ER -